In-Silico Tool for Predicting, Scanning, and Designing Defensins - Kaur, Dilraj; Patiyal, Sumeet; Arora, Chakit; Singh, Ritesh; Lodhi, Gaurav; Raghava, Gajendra P. S. (Frontiers in Immunology, 2021)
Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to host, either by killing microbes directly or indirectly by activating immune system. In the era of antibiotic resistance, there is a need to develop fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called main dataset that contains 1036 defensins and 1035 AMPs (Anti-Microbial Peptides) that are (non-defensins) to understand difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning techniques-based models on main dataset using a wide range of peptide features. Our SVM (Support Vector Machine) based model discriminates defensins and AMPs with MCC 0.88 and AUC 0.98 on validation set of main dataset. In addition, we created an alternate dataset that consists of 1036 defensins and 1054 non-defensins obtained from Swiss-Prot. Models were also developed on alternate dataset to predict defensins. Our SVM based model achieved maximum MCC 0.96 with AUC 0.99 on validation set of alternate dataset. All models were trained, tested and validated using standard protocols. Finally, we developed a web-based service ‘DefPred’ to predict defensins, scan defensins in proteins and to design best defensins from their analogs. Standalone and web-server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred .